The data value of hyperspectral measurements on ice and snow cover is strongly impacted by the availability of data services, where spectral libraries are integrated to detailed descriptions of the ...observed surface cover. For snow and ice cover, we present an updated version of the Snow/Ice Spectral Archive (SISpec 2.0), which has been integrated into a web portal characterized by different functionalities. The adopted metadata scheme features basic geographic data, information about the acquisition setup, and parameters describing the different surface types. While the implementation of the IACS Classification of Seasonal Snow on the Ground is the core component for snow cover, ice cover is approached using different parameters associated with its surface roughness and location. The web portal is not only a visualization tool, but also supports interoperability functionalities, providing data in the NetCDF file format. The availability of these functionalities sets the foundation for sharing a novel platform with the community and is an interesting tool for calibrating and validating data and models.
In this work, X band images acquired by COSMO-SkyMed (CSK) on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary ...results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR) images (Landsat-8 and CSK) in separating snow/no-snow areas and in detecting wet snow. The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS) was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R² = 0.77, and root mean square error, RMSE = 1.1 dB, with
-value <0.05.
Abstract
Snow represents a fundamental water resource for mountain and lowland areas. Changes in the frequency and magnitude of snow droughts can significantly impact societies and ecosystems that ...rely on snowmelt to satisfy their water demands. Here we documented and quantified the snow drought that affected the Italian Alps during the early 2020s. We used 15 long-term snow-depth series (period 1930–2023, elevation range: 864–2200 m a.s.l.) to simulate the snow water equivalent (SWE), in conjunction with climatic reanalysis data and river discharge observations. We found that the March SWE anomaly in 2022 reached the lowest value in the last century, due to an unprecedented combination of drier- and warmer-than-normal conditions in the period December 2021–March 2022. This event contributed to causing critical hydrological conditions in the Po and Adige rivers which, during summer 2022, experienced the worst hydrological drought ever recorded. Despite its unprecedented magnitude, the snow drought in 2022 is part of a recent pattern of increased intensity and frequency of snow-drought events since the 1990s, due to the combined increasing occurrence of warmer- and drier-than-normal climatic conditions during the snow season. Remarkably, three out of the five most severe snow-drought events occurred in the last five years, with exceptional snow-drought conditions even occurring in the last two consecutive winters, 2022 and 2023. The snow-drought conditions that occurred in the early 2020s in the Italian Alps warn of the pressing need for the implementation of impact mitigation measures to adapt to the fast changing snow and climatic conditions.
At the East Antarctic deep ice core drilling site Dome C, daily precipitation measurements were initiated in 2006 and are being continued until today. The amounts and stable isotope ratios of the ...precipitation samples as well as crystal types are determined. Within the measuring period, the two years 2009 and 2010 showed striking contrasting temperature and precipitation anomalies, particularly in the winter seasons. The reasons for these anomalies are analysed using data from the mesoscale atmospheric model WRF (Weather Research and Forecasting Model) run under the Antarctic Mesoscale Prediction System (AMPS). 2009 was relatively warm and moist due to frequent warm air intrusions connected to amplification of Rossby waves in the circumpolar westerlies, whereas the winter of 2010 was extremely dry and cold. It is shown that while in 2010 a strong zonal atmospheric flow was dominant, in 2009 an enhanced meridional flow prevailed, which increased the meridional transport of heat and moisture onto the East Antarctic plateau and led to a number of high-precipitation/warming events at Dome C. This was also evident in a positive (negative) SAM (Southern Annular Mode) index and a negative (positive) ZW3 (zonal wave number three) index during the winter months of 2010 (2009). Changes in the frequency or seasonality of such event-type precipitation can lead to a strong bias in the air temperature derived from stable water isotopes in ice cores.
The relation between the fraction of snow cover and the spectral behavior of the surface is a critical issue that must be approached in order to retrieve the snow cover extent from remotely sensed ...data. Ground-based cameras are an important source of datasets for the preparation of long time series concerning the snow cover. This study investigates the support provided by terrestrial photography for the estimation of a site-specific threshold to discriminate the snow cover. The case study is located in the Italian Alps (Falcade, Italy). The images taken over a ten-year period were analyzed using an automated snow-not-snow detection algorithm based on Spectral Similarity. The performance of the Spectral Similarity approach was initially investigated comparing the results with different supervised methods on a training dataset, and subsequently through automated procedures on the entire dataset. Finally, the integration with satellite snow products explored the opportunity offered by terrestrial photography for calibrating and validating satellite-based data over a decade.
The correct derivation of paleotemperatures from ice cores requires exact knowledge of all processes involved before and after the deposition of snow and the subsequent formation of ice. At the ...Antarctic deep ice core drilling site Dome C, a unique data set of daily precipitation amount, type, and stable water isotope ratios is available that enables us to study in detail atmospheric processes that influence the stable water isotope ratio of precipitation. Meteorological data from both automatic weather station and a mesoscale atmospheric model were used to investigate how different atmospheric flow patterns determine the precipitation parameters. A classification of synoptic situations that cause precipitation at Dome C was established and, together with back-trajectory calculations, was utilized to estimate moisture source areas. With the resulting source area conditions (wind speed, sea surface temperature, and relative humidity) as input, the precipitation stable isotopic composition was modeled using the so-called Mixed Cloud Isotope Model (MCIM). The model generally underestimates the depletion of 18O in precipitation, which was not improved by using condensation temperature rather than inversion temperature. Contrary to the assumption widely used in ice core studies, a more northern moisture source does not necessarily mean stronger isotopic fractionation. This is due to the fact that snowfall events at Dome C are often associated with warm air advection due to amplification of planetary waves, which considerably increases the site temperature and thus reduces the temperature difference between source area and deposition site. In addition, no correlation was found between relative humidity at the moisture source and the deuterium excess in precipitation. The significant difference in the isotopic signal of hoarfrost and diamond dust was shown to disappear after removal of seasonality. This study confirms the results of an earlier study carried out at Dome Fuji with a shorter data set using the same methods.
•Analysis of 1930–2020 variability of Italian Alps Standardised SWE Index (SSWEI)•SSWEI shows large variability, with lowermost values in 1991–2020.•Change in relation between SSWEI and ...teleconnection indices after the 1980s.•Negative SSWEI is driven by increasing air temperature from the 1990s.•The last decades experienced unprecedented and persistent snow-drought conditions.
Snow stores a significant amount of water in mountain regions. The decrease of water storage in the snowpack can have relevant impacts on water supply for mountain and lowland areas that rely on snow melting. In this work, we modelled the Snow Water Equivalent (SWE) using daily snow depth (HS) data obtained from 19 historical HS measurement stations located in the southern European Alps (Italy). Then, we analysed the long-term (1930–2020) variability of the monthly Standardised SWE Index (SSWEI) and its links with climate change and large-scale atmospheric forcings (teleconnection indices). We found a marked variability in monthly SSWEI, with the lowermost values generally occurring in the last few decades (1991–2020), irrespective of elevation. In this recent period, highly negative values occurred at the snow season tails, mostly in spring. We found large-scale atmospheric patterns (North Atlantic Oscillation, Atlantic Multi-decadal Oscillation, and Artic Oscillation) and precipitation to be interconnected with SSWEI oscillations, although this relation changed after the 1980s, especially at low and medium elevations. This change occurred in correspondence of highly positive air temperature anomalies. In the last decades, we found increasing air temperature to be the main driver for the pronounced snow mass loss and persistent snow-drought conditions.
This paper is focused to the study of spatial variability of snowcover indifferent polar environmental contests. To this aim we present about 200 stratigraphic profiles of the snowpack, collected in ...the last years (1998–2015), in selected sites along the coast of the Brøggerhalvøya peninsula (Svalbard Islands). The different layers in the snowpack were identified and classified according to the International Standards, in terms of grain size, grain types, hardness and density. These data were used to calculate average values of hardness and density for each layer showing the same grain type characteristics. These snow stratigraphic profiles, coupled with meteorological observations, represent a valuable dataset to describe the relationship in the snowpack between pluriannial and seasonal snow and to study the characteristics of seasonal snow covers in the Svalbard region and their transformation during spring season. The stratigraphic dataset analysis seem to confirm an Arctic snow climate maritime for Brøggerhalvøya area as evidenced for a different site in Svalbard Island (Longyearbyen) by other authors.
This study aims at relating the stickiness parameter (<inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>) of the dense media radiative transfer theory in quasi-crystalline ...approximation of Mie scattering of densely packed sticky spheres (DMRT-QMS), to the physical parameters of the layered snowpack. A relationship has been derived to express <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>, which modulates the attractive contact force between ice spheres, as a function of ice volume fraction (<inline-formula> <tex-math notation="LaTeX">\phi </tex-math></inline-formula>) and coordination number (<inline-formula> <tex-math notation="LaTeX">n_{c} </tex-math></inline-formula>). Since <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula> is not a measurable parameter, this is a step forward with respect to what is commonly made in the literature, where <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula> is assumed as an arbitrary parameter, generally ranging between 0.1 and 0.3, to fit simulated backscattering data with those measured. As a first validation, DMRT-QMS was integrated with the SNOWPACK model to simulate backscattering at X-band (9.6 GHz) driven by nivo-meteorological data acquired on a test area located in Monti Alti di Ornella, Italy. The simulations were compared with Synthetic Aperture Radar COSMO-SkyMed (CSK) satellite observations. The results show a significant agreement (<inline-formula> <tex-math notation="LaTeX">R^{2} =0.68 </tex-math></inline-formula>), although for a limited dataset of eight points in a unique winter season.
The Snow Water Equivalent (SWE), combining the information of snow depth (Hs) and snow bulk density (ρb) is a necessary variable for snow-hydrological studies and applications, as well as, for ...ecological function or avalanche forecasting. The SWE direct measurement is challenging, and estimating the SWE from the single Hs measurements presents many advantages compared to the direct SWE measurement or the implementation of complex model needing to be fed by local meteorological data. In this study we propose a spatial and temporal variability description of the SWE, Hs and ρb and compare existing approaches over the Italian Alps. Finally, we propose a simple parametrization, introducing non-linearity in the snow bulk density variability. The resulting overall uncertainty on SWE is 15.6%. The proposed model has the potential to be a valuable tool to estimate the SWE from the only HS measurement in the Italian Alps, presenting even better performances during the late season (13.9%, 12.9% and 14.3% in March, April and May, respectively) that makes it particularly suitable for snow-hydrology studies.
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•70% of the SWE total variance is explained by the only snow depth variance.•Bulk density variance mainly affects the SWE in terms of late seasonal dynamics.•No significant correlation between Hs and ρb is observed in the Italian Alps.•Regional quadratic dependency on DOY gives the best results.•The resulting SWE uncertainty is 15.6% with best performances during the late season.
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